def get_gal(Args, cat, get_seds=False):
"""Returns parametric bulge+dik galaxy (galsim Chromatic object).
@Args galaxy parametrs (same for all catsim galaxies)
@cat catsim row with galaxy parameters (different for every galaxy)
@get_seds if True, returns bulge and disk SEDs (default:False).
"""
norm_d = cat['fluxnorm_disk'] # * 30 * np.pi * (6.67 * 100 / 2.)**2
Args.d_SED = get_catsim_SED(cat['sedname_disk'],
redshift=cat['redshift'],
a_v=cat['av_d'],
r_v=cat['rv_d'],
model='ccm89') * norm_d
norm_b = cat['fluxnorm_bulge'] # * 30 * np.pi * (6.67 * 100 / 2.)**2
Args.b_SED = get_catsim_SED(cat['sedname_bulge'],
redshift=cat['redshift'],
a_v=cat['av_b'],
r_v=cat['rv_b'],
model='ccm89') * norm_b
# composite SED
seds = [Args.b_SED * (1 / norm_b), Args.d_SED * (1 / norm_d)]
Args.c_SED = Args.b_SED + Args.d_SED
PSF = cg_fn.get_gaussian_PSF(Args)
gal = cg_fn.get_gal_cg(Args)
con = galsim.Convolve([gal, PSF])
if get_seds:
return gal, PSF, con, seds
else:
return gal, PSF, con
def get_CRG(in_p, SNRs, row, rng):
"""Create CRG for a given input galaxy parametrs.
Bulge + Disk galaxy is created, convolved with HST PSF, drawn in HST V and
I bands for 1 second exposure. Correlated noise (from AEGIS images)
is added to each image. SNR in a gaussian elliptical aperture is computed.
cgr1: The galaxy +psf images + noise correlation function is provided as
input to CRG with default polynomial SEDs. The input galaxy images are
padded with noise. This enables us to to draw the CRG image larger than
the input image, and not have boundary edges.
crg2: same as crg1 except the true SEDS of bulge and disk are provided
as input to CRG.
"""
# HST scale
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
PSF = cg_fn.get_gaussian_PSF(in_p)
gal = cg_fn.get_gal_cg(in_p)
con = galsim.Convolve([gal, PSF])
images, bands = get_HST_im(con)
psf_v = cg_fn.get_eff_psf(PSF, in_p.c_SED, bands[0])
psf_i = cg_fn.get_eff_psf(PSF, in_p.c_SED, bands[1])
eff_PSFs = [psf_v, psf_i]
images, cfs = get_HST_im_noise(images, SNRs, row, rng)
crg1 = galsim.ChromaticRealGalaxy.makeFromImages(images=images,
bands=bands,
xis=cfs,
PSFs=eff_PSFs)
return crg1
def cg_other_est():
"""Test all HSM shear estimators give same value as REGAUSS (default)"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
psf_args = cg_fn.psf_params()
meas_args = cg_fn.meas_args(shear_est='KSB')
meas_args.bp = filt
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
meas_args.shear_est = 'BJ'
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
meas_args.shear_est = 'LINEAR'
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
def same_sed():
"""bulge and disk have same sed, cg bias must be zero"""
in_p = cg_fn.LSST_Args(disk_SED_name='E')
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0], [0, 0],
decimal=5)
def achr_psf():
"""LSST PSF is chromatic, CG bias must be 0"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params(alpha=0)
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0, 0],
decimal=5)
def with_cg():
"""Raise error if no cg bias in default setting"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_raises(AssertionError,
np.testing.assert_array_almost_equal,
(gcg / gnocg - 1).T[0], [0, 0], decimal=5)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
def with_CRG():
"""Raise error if no cg bias in default setting"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
CRG1, CRG2 = cg_fn.get_CRG_basic(gal, in_p)
gcg1, gnocg1 = cg_fn.calc_cg_crg(CRG1, meas_args, psf_args)
gcg2, gnocg2 = cg_fn.calc_cg_crg(CRG2, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg1 / gnocg1 - 1).T[0],
[0.00128506, 0.0012862],
decimal=5)
np.testing.assert_array_almost_equal((gcg2 / gnocg2 - 1).T[0],
[0.00106303, 0.00106446],
decimal=5)
def get_lsst_para(in_p):
"""Create parametric bulge+disk galaxy for input parametrs."""
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
return gal